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1. Stochastic model predictive controller for wind farm frequency regulation in waked conditions

   https://doi.org/10.1016/j.epsr.2022.108543  

   Chen, Sunny; Mathieu, Johanna L.; Seiler, Peter (October 2022, Electric Power Systems Research)
2. A model for wind farm management with option interactions

   https://doi.org/10.1111/poms.13721  

   Bensoussan, Alain; Chevalier‐Roignant, Benoît; Rivera, Alejandro (April 2022, Production and Operations Management)

   Abstract A renewable energy site can expand its power generation capacity by an endogenous amount but may also want to shut down to save on fixed operating costs and interest payments if the market prospects deteriorate. We model such circumstances and derive managerial implications that help us explain real‐world conundrums, illustrating the intricate interactions between the operational decision to build up capacity and the financial decision to exit an industry. Shutting down may be delayed in the hope of expanding capacity upon recovery; an expansion may also be delayed in the presence of a valuable exit option. Numerical extensions provide further managerial insights. In particular, the presence of fixed or proportional financing costs may lead the firm to delay its expansion decision, but the scale of investment will only be affected by proportional costs. If herding behavior causes equipment prices to increase (respectively, decrease) when electricity prices are high (respectively, low), managers should invest earlier (respectively, later) and more (respectively, less) while equipment prices are low (respectively, high). Furthermore, although volume swings (due to capacity decommissionings and expansions) are marked in a homogeneous industry (when the default and expansion thresholds are reached), heterogeneity in the population of wind farms smooths out such effects. 

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3. A Wake Modeling Paradigm for Wind Farm Design and Control

   https://doi.org/10.3390/en12152956  

   Shapiro, Carl R.; Starke, Genevieve M.; Meneveau, Charles; Gayme, Dennice F. (August 2019, Energies)

   null (Ed.)

   Wake models play an integral role in wind farm layout optimization and operations where associated design and control decisions are only as good as the underlying wake model upon which they are based. However, the desired model fidelity must be counterbalanced by the need for simplicity and computational efficiency. As a result, efficient engineering models that accurately capture the relevant physics—such as wake expansion and wake interactions for design problems and wake advection and turbulent fluctuations for control problems—are needed to advance the field of wind farm optimization. In this paper, we discuss a computationally-efficient continuous-time one-dimensional dynamic wake model that includes several features derived from fundamental physics, making it less ad-hoc than prevailing approaches. We first apply the steady-state solution of the model to predict the wake expansion coefficients commonly used in design problems. We demonstrate that more realistic results can be attained by linking the wake expansion rate to a top-down model of the atmospheric boundary layer, using a super-Gaussian wake profile that smoothly transitions between a top-hat and Gaussian distribution as well as linearly-superposing wake interactions. We then apply the dynamic model to predict trajectories of wind farm power output during start-up and highlight the improved accuracy of non-linear advection over linear advection. Finally, we apply the dynamic model to the control-oriented application of predicting power output of an irregularly-arranged farm during continuous operation. In this application, model fidelity is improved through state and parameter estimation accounting for spanwise inflow inhomogeneities and turbulent fluctuations. The proposed approach thus provides a modeling paradigm with the flexibility to enable designers to trade-off between accuracy and computational speed for a wide range of wind farm design and control applications. 

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4. LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes

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   Gowdanakatte, Shwetha; Abdelgawad, Mahmoud; Ray, Indrakshi (July 2024, IEEE)